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Arynes are versatile intermediates for organic synthesis and now they can be accessed from arenes in one or two-pot sequencesviaaryl thianthrenium (in situ) and aryl iodonium (isolated) intermediates, respectively. 

Arynes hold immense potential as reactive intermediates in organic synthesis as they engage in a diverse range of mechanistically distinct chemical reactions. However, the poor functional group compatibility of generating arynes or their precursors has stymied their widespread use. Here, we show that generating arynes by deprotonation of an arene and elimination of an “onium” leaving group is mild, efficient and broad in scope. This is achieved by using aryl(TMP)iodonium salts (TMP = 2,4,6-trimethoxyphenyl) as the aryne precursor and potassium phosphate as the base, and a range of arynophiles are compatible. Additionally, we have performed the first quantitative analysis of functional group compatibility for several methods to generate arynes, including the method developed here and the current state of the art. Finally, we show that a range of “sensitive” functional groups such as Lewis and Brønsted acids and electrophiles are compatible under our conditions. 

Arylboron compounds are widely available and synthetically useful reagents in which the boron group is typically substituted. Herein, we show that the boron group and orthohydrogen atom are substituted in a formal cycloaddition reaction. This transformation is enabled by a one-pot sequence involving diaryliodonium and aryne intermediates. The scope of arylboron reagents and arynophiles is demonstrated, and the method is applied to the formal synthesis of an investigational drug candidate. 

Improving the scalability of probabilistic model checking (PMC) tools is crucial to the verification of real-world system designs. The STAMINA infinite-state PMC tool achieves scalability by iteratively constructing a partial state space for an unbounded continuous-time Markov chain model, where a majority of the probability mass resides. It then performs time-bounded transient PMC. It can efficiently produce an accurate probability bound to the property under verification. We present a new software architecture design and the C++ implementation of the STAMINA 2.0 algorithm, integrated with the STORM model checker. This open-source STAMINA implementation offers a high degree of modularity and provides significant optimizations to the STAMINA 2.0 algorithm. Performance improvements are demonstrated on multiple challenging benchmark examples, including hazard analysis of infinite-state combinational genetic circuits, over the previous STAMINA implementation. Additionally, its design allows for future customizations and optimizations to the STAMINA algorithm. 

In synthetic biology, combinational circuits are used to program cells for various new applications like biosensors, drug delivery systems, and biofuels. Similar to asynchronous electronic circuits, some combinational genetic circuits may show unwanted switching variations (glitches) caused by multiple input changes. Depending on the biological circuit, glitches can cause irreversible effects and jeopardize the circuit’s functionality. This paper presents a stochastic analysis to predict glitch propensities for three implementations of a genetic circuit with known glitching behavior. The analysis uses STochastic Approximate Model-checker for INfinite-state Analysis (STAMINA), a tool for stochastic verification. The STAMINA results were validated by comparison to stochastic simulation in iBioSim resulting in further improvements of STAMINA. This paper demonstrates that stochastic verification can be utilized by genetic designers to evaluate design choices and input restrictions to achieve a desired reliability of operation.